System, method and computer program product for assessing the capabilities of a conversation agent via black box testing

ABSTRACT

A conversational agent capability assessment method, system, and computer program product, includes assessing a performance, a personality and a cognitive trait of a conversational agent based on natural written language by a second conversational agent by combining an analysis of a plurality of metrics that each compare a metric from the conversational agent with a metric from the second conversational agent and producing a report detailing the performance, the personality, and the cognitive trait of the conversational agent.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of U.S. patentapplication Ser. No. 15/338,695, filed on Oct. 31, 2016, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to a conversational agentcapability assessment method, and more particularly, but not by way oflimitation, to a system, method, and computer program product forassessing performance, personality and cognitive traits ofconversational agents based on natural written language by otherconversational agents.

Testing a conversational agent is not a trivial task. A conversationalagent has many aspects to be tested from the performance across multipleusers to matching the best response for a given answer. Further,conversational agents also may interact amongst each other (i.e., ahotel room booking agent may interact with a rental car booking agent ora first version (V1) of a conversational agent with a second version(V2) of the conversational agent) which adds an additional layer ofdifficulty for testing.

SUMMARY

In an exemplary embodiment, the present invention can provide acomputer-implemented conversational agent capability assessment method,the method including obtaining data to create scenarios for testing aconversational agent, performing a set of tests using a scenario of thecreated scenarios to assess a capability of the conversational agent,and comparing a result of the capability from a set of tests with anexpected result of the scenario.

One or more other exemplary embodiments include a computer programproduct and a system.

Other details and embodiments of the invention will be described below,so that the present contribution to the art can be better appreciated.Nonetheless, the invention is not limited in its application to suchdetails, phraseology, terminology, illustrations and/or arrangements setforth in the description or shown in the drawings. Rather, the inventionis capable of embodiments in addition to those described and of beingpracticed and carried out in various ways and should not be regarded aslimiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention will be better understood from the followingdetailed description of the exemplary embodiments of the invention withreference to the drawings, in which:

FIG. 1 exemplarily shows a high-level flow chart for a conversationalagent capability assessment method 100;

FIG. 2 exemplarily shows a system 200 for conversational agentcapability assessment;

FIG. 3 exemplarily shows a report generated by the Step 105 afterrunning testing scenarios;

FIG. 4 depicts a cloud computing node 10 according to an embodiment ofthe present invention;

FIG. 5 depicts a cloud computing environment 50 according to anembodiment of the present invention; and

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-6, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity.

With reference now to the example depicted in FIG. 1, the conversationalagent capability assessment method 100 includes various steps to assessperformance, personality and cognitive traits of conversational agentsbased on natural written language by other conversational agents,combine different metrics that analyze different aspects such ascognitive ability, performance and that affect the process of makingthose agents more intelligent, and the user adoption, and provide a wayto test not familiar conversational agents systems (e.g., black boxtesting in which there is no knowledge (or little knowledge) of how thesystem was implemented). As shown in at least FIG. 4, one or morecomputers of a computer system 12 according to an embodiment of thepresent invention can include a memory 28 having instructions stored ina storage system to perform the steps of FIG. 1.

Thus, the conversational agent capability assessment method 100according to an embodiment of the present invention may act in a moresophisticated, useful and cognitive manner, giving the impression ofcognitive mental abilities and processes related to knowledge,attention, memory, judgment and evaluation, reasoning, and advancedcomputation. A system can be said to be “cognitive” if it possessesmacro-scale properties—perception, goal-oriented behavior,learning/memory and action—that characterize systems (i.e., humans)generally recognized as cognitive.

Although one or more embodiments (see e.g., FIGS. 4-6) may beimplemented in a cloud environment 50 (see e.g., FIG. 5), it isnonetheless understood that the present invention can be implementedoutside of the cloud environment.

The method 100 and system 200 can provide a framework to test aconversational agent in several aspects emulating a real user embodiedinto a conversational agent. A conversational agent (i.e., chat box)typically includes a graphical interface for users to interact with theagent, a natural language processor to convert each question to a set ofintentions, a knowledge base or corpus in which the bot retrievesanswers based on the user intention, and a cognitive ability to adjustanswers to the context of present dialog (i.e., the inputquestions/outputs answers). The method 100 and system 200 as describedlater can evaluate a conversational agent from performance metrics tothe agent's cognitive abilities. The method 100 and system 200 not onlyprovide a useful test for all these aspects for chat bot developers(i.e., which saves massive amounts of manual testing, especially forlong utterances), but also provides a benefit for interface designersthat can compare the test results with an ideal solution.

It is noted that although the embodiments described herein generallyrefer to a textual input for the chat box, the invention is not limitedthereto. That is, the method 100 and system 200 can include a modulethat translates speech-to-text and text-to-speech, extending theapplicability of the conversational speech agents. For example, Siri®may be an example of a speech input conversational agent.

In step 101 (or the dialog generator 201), data is obtained to createscenarios (e.g., generate dialog to replicate scenarios) for testing theconversational agents. The data can be obtained from, for example,call-center logs, Watson™ conversational services, chat box logs, etc.The scenarios includes a plurality of user inputs intended to cause anaspect of the conversational agent to be triggered. For example, a userinput of one scenario can include several typographical errors to testthe chat bot's ability to determine a user's intent even withtypographical errors. Another scenario can include a user input ofseveral questions using different synonyms to determine the chat bot'sability to process a dialogue flow.

In step 102 (or by the test performer 202), a set of tests are performedusing the scenarios created from the data to assess capabilities (oraspects) of the conversational agent. The set of tests can include, forexample, testing the conversational agent for its natural languageability by testing its ability to recognize language protocols to starta conversational, testing the performance metrics of the conversationalagent by running scenarios to test response time or how much time anagent takes to process a question and gives an answer, testing a patterndialogue flow by using scenarios to test if a conversational agent isable to identify patterns in the text such as emoticons, symbols, etc.,testing a response to unexpected situations by using scenarios thatcreate, for example, typos, slangs, user aggressive language, etc.,testing a dialog personalization by running scenarios to determine if aconversational agent is capable of changing its tone according to aname, a gender, an age, etc. of the user, testing a knowledge base ofthe conversational agent by running a scenario including both on-domainand off-domain questions (e.g., today's date, what is the capital ofUSA, etc.), testing cognitive measures of the chat bot by using severaldifferent taxonomies in scenarios (e.g., Bloom's, Piaget, etc.), testingfor conversational speech systems by running scenarios that test noiselevels, speech recognition, tone recognition, etc., or the like.

Although various examples of different tests and testing scenarios aregiven, the invention is not limited thereto. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments.

That is, several testing metrics in combination or individually areconsidered for automatically assessing the performance or the capacityof a conversational agent.

In step 103 (or by the statistic analyzer 203), results from performingthe set of tests in step 102 are compared with a set of expectedresults. For the target recipients (e.g., conversational agent designersand developers), a comparison among other conversational agents canprovide valuable insights into improvements to their systems. Forexample, scenarios comparing two or more different conversational agentsystems or two different versions (e.g., V1 and V2) of the sameconversational agent. That is, the scope and the capabilities may beknown in previous versions of the software and can be compared with the“black box” testing via step 102 to determine improvements ordeficiencies of the chat bot.

In step 104 (or by the system 200), the results from the comparingamongst other conversational agents or previous versions of the sameagent are stored in a database for future testing. Further, newscenarios for testing can be determined from the comparison results. Forexample, if the results show an increase in processing time for answers,a new scenario can be run to “stress test” the chat bot to determine alimit on the processing times.

In step 105 (or by the statistic analyzer 203), reports are generatedusing the testing data. The reports indicate the test results for eachof the different scenarios such that a human developer can optimize thechat bot. FIG. 3 exemplarily shows a comparison among testing executionsof different versions of the same conversational agent or differentconversations agents (e.g. competitors comparison). Each metric iseither displayed using a score (e.g., for performance metrics can be theresponse time in seconds) or graphically if metric comprises manymeasures such as the cognitive metric. The report provides a summary foreach metric (e.g., last column of FIG. 3) comparing the bestconversation agent for that metric. Finally, the last row (OverallSummary for each Agent) describes an overall comparison for thatconversational agent. For example, conversational agent X respond betterto unexpected results and needs to improve his natural languageprocessor module.

In step 106 (improvement suggestions 204), an action to be made by ahuman analyst is suggested by analyzing the reports of the test results.That is, fixes or remedies to the agent's problems or issues may besuggested. For example, if the reports generated by step 105 indicatethat the natural language processor became inefficient for version 2.0of a given conversation agent X. The analyst can suggest that developerscheck what changes in implementation have caused the problem. Forexample, certain groups of questions were not covered in a given versionof the conversation agent, the system than suggest that these questionsshould be added to the machine-leaning model of the agent.

In some embodiments, testing different user personalities, for example,by analyzing the type of words used by the conversational agent can bedone to benefit developers. The results may reflect a less friendlycommunication between the agent and the end-user that can be adjusted(if needed) by the developers (e.g., dialog personalization).

In other embodiments, a high response time may indicate that theconversational system has limitations on its architecture. Very lowresponse time may indicate that the end-user may find it difficult toread the displayed answers. And, a high number of repetitive answersfrom the conversational agent can indicate problems with its NLP module.This information can be useful for chat bot developers to adjust thechat bot to meet the specification requirements of the end user.

It is noted that “conversation agent” and “conversational agent” areused interchangeably and mean the same thing for the scope of theinvention.

Exemplary Aspects, Using a Cloud Computing Environment

Although this detailed description includes an exemplary embodiment ofthe present invention in a cloud computing environment, it is to beunderstood that implementation of the teachings recited herein are notlimited to such a cloud computing environment. Rather, embodiments ofthe present invention are capable of being implemented in conjunctionwith any other type of computing environment now known or laterdeveloped.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablenode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth herein.

Although cloud computing node 10 is depicted as a computer system/server12, it is understood to be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with computersystem/server 12 include, but are not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop circuits, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems orcircuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

Referring again to FIG. 4, computer system/server 12 is shown in theform of a general-purpose computing circuit. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, an exemplary set of functional abstractionlayers provided by cloud computing environment 50 (FIG. 5) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the conversational agent capability assessment method100.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A computer-implemented conversational agentcapability assessment method, the method comprising: assessing aperformance, a personality and a cognitive trait of a conversationalagent based on natural written language by a second conversational agentby combining an analysis of a plurality of metrics that each compare ametric from the conversational agent with a metric from the secondconversational agent; and producing a report detailing the performance,the personality, and the cognitive trait of the conversational agent byrunning a comparison between a result of the assessing and an expectedresult, wherein the second conversation agent deploys at least twodifferent types of personalities, and wherein the assessing comprises:obtaining data to create at least one scenario for testing theconversational agent; and performing a set of tests using a scenario ofthe at least one scenario created to assess a capability of theconversational agent including the performance, the personality and thecognitive trait.
 2. The computer-implemented method of claim 1, whereineach of the at least one created scenario comprises a user input tocause an aspect of the conversational agent to be tested.
 3. Thecomputer-implemented method of claim 2, wherein the performing the setof tests comprises testing at least one of: a natural language abilityof the conversational agent; performance metrics of the conversationalagent; pattern dialogue flow of the conversational agent; a response toan unexpected user input by the conversational agent; a dialogpersonalization of the conversational agent; a knowledge base of theconversational agent; and a cognitive ability of the conversationalagent.
 4. The computer-implemented method of claim 2, wherein theexpected result includes at least one of: a capability of a differentconversational agent; and a capability of a different version of a sameconversational agent being tested.
 5. The computer-implemented method ofclaim 2, wherein the at least one created scenario include aspeech-to-text and a text-to-speech conversion to assess the capabilityof the conversational agent for both of a textual input conversationalagent and a speech input conversational agent.
 6. Thecomputer-implemented method of claim 2, further comprising storing theresult from the set of tests to be used for a future comparison by thecomparing.
 7. The computer-implemented method of claim 2, furthercomprising generating a report of the result of the capability of theconversational agent for a human user.
 8. The computer-implementedmethod of claim 7, further comprising suggesting an action for the humanuser to modify the conversational agent based on the report of theresult of the capability of the conversational agent.
 9. Thecomputer-implemented method of claim 1, embodied in a cloud-computingenvironment.
 10. A computer program product for conversational agentcapability assessment, the computer program product comprising acomputer-readable storage medium having program instructions embodiedtherewith, the program instructions executable by a computer to causethe computer to perform: assessing a performance, a personality and acognitive trait of a conversational agent based on natural writtenlanguage by a second conversational agent by combining an analysis of aplurality of metrics that each compare a metric from the conversationalagent with a metric from the second conversational agent; and producinga report detailing the performance, the personality, and the cognitivetrait of the conversational agent by running a comparison between aresult of the assessing and an expected result, wherein the secondconversation agent deploys at least two different types ofpersonalities, and wherein the assessing comprises: obtaining data tocreate at least one scenario for testing the conversational agent; andperforming a set of tests using a scenario of the at least one scenariocreated to assess a capability of the conversational agent including theperformance, the personality and the cognitive trait.
 11. The computerprogram product of claim 10, and wherein each of the at least onecreated scenario comprises a user input to cause an aspect of theconversational agent to be tested.
 12. The computer program product ofclaim 11, wherein the performing the set of tests comprises testing atleast one of: a natural language ability of the conversational agent;performance metrics of the conversational agent; pattern dialogue flowof the conversational agent; a response to an unexpected user input bythe conversational agent; a dialog personalization of the conversationalagent; a knowledge base of the conversational agent; and a cognitiveability of the conversational agent.
 13. The computer program product ofclaim 11, wherein the expected result includes at least one of: acapability of a different conversational agent; and a capability of adifferent version of a same conversational agent being tested.
 14. Thecomputer program product of claim 11, wherein the at least one createdscenario includes a speech-to-text and a text-to-speech conversion toassess the capability of the conversational agent for both of a textualinput conversational agent and a speech input conversational agent. 15.The computer program product of claim 11, further comprising storing theresult from the set of tests to be used for a future comparison by thecomparing.
 16. The computer program product of claim 11, furthercomprising generating a report of the result of the capability of theconversational agent for a human user.
 17. The computer program productof claim 16, further comprising suggesting an action for the human userto modify the conversational agent based on the report of the result ofthe capability of the conversational agent.
 18. A conversational agentcapability assessment system, said system comprising: a processor; and amemory, the memory storing instructions to cause the processor toperform: assessing a performance, a personality and a cognitive trait ofa conversational agent based on natural written language by a secondconversational agent by combining an analysis of a plurality of metricsthat each compare a metric from the conversational agent with a metricfrom the second conversational agent; and producing a report detailingthe performance, the personality, and the cognitive trait of theconversational agent by running a comparison between a result of theassessing and an expected result, wherein the second conversation agentdeploys at least two different types of personalities, and wherein theassessing comprises: obtaining data to create at least one scenario fortesting the conversational agent; and performing a set of tests using ascenario of the at least one scenario created to assess a capability ofthe conversational agent including the performance, the personality andthe cognitive trait.
 19. The system of claim 18, and wherein each of theat least one created scenario comprises a user input to cause an aspectof the conversational agent to be tested.
 20. The system of claim 18,embodied in a cloud-computing environment.